Deep learning models usually only produce the mean values of predictions, for example, the output of a DNN-based visual odometry model is a 6-dimensional relative pose vector, i.e. the translation and rotation. In order to capture the uncertainty of deep models, learning models can be augmented into a Bayesian model [1]}, [2]}. The uncertainty from Bayesian models is broadly categorized into Aleatoric uncertainty and epistemic uncertainty: Aleatoric uncertainty reflects observation noises, e.g. sensor measurement or motion noises; epistemic uncertainty captures the model uncertainty [2]}.
In the context of this survey, we focus on the work of estimating uncertainty on the specific task of localization and mapping, with regard to their usages, i.e. whether they capture the uncertainty with the purpose of motion tracking or scene understanding.
